Read-Head Off-Track Detector Calibration
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Solution Overview
Problem
In magnetic recording systems, as areal densities increase, read heads face challenges in accurately determining off-track deviations from nominal positions, which affects data reading performance and requires optimized system parameters for improved performance.
Innovation Solution
A method and device for calibrating the relationship between signals from a current track and adjacent tracks by accumulating data values, determining an inverted covariance matrix without matrix inversion, using adaptive filtering and noise component correlation to determine error and noise components, and adjusting filter coefficients for precise off-track detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If read heads operate at higher areal densities with shingled magnetic recording, then storage capacity increases, but off-track detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary calibration by accumulating data values and computing covariance matrices before actual data reading operations. This pre-computed calibration information is stored and applied during normal operation to maintain accurate off-track detection despite increased areal density and shingled recording conditions.
Solution Approach 2:
The system continuously monitors read head position and uses feedback from the detected signals to adjust and refine the calibration. The covariance matrix computation incorporates feedback loops that adapt to changing operational conditions, ensuring sustained detection accuracy as areal density increases.
2Measurement precision
If matrix inversion operations are performed to determine covariance matrices, then calibration accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs matrix accumulation and covariance computation during preliminary calibration phases rather than during real-time data reading. By pre-computing and storing calibration data in multi-dimensional space, the system avoids complex matrix inversion operations during operational data processing, significantly reducing computational burden while maintaining calibration accuracy.
Data Source
AI summary
In a data storage device having a storage medium, wherein data is written to tracks on the storage medium, data for each track including a preamble, and wherein the preamble in any current track is orthogonal to the preamble in any track adjacent to the current track, and wherein data accumulated in a multi-dimensional space is representative of a relationship between signals from the current track and signals from at least one adjacent track, the relationship between the signals from the current track and the signals from the at least one adjacent track is calibrated by, for each respective position out of a plurality of positions in the multi-dimensional space, accumulating a plurality of data values for the respective position, and determining, from the plurality of data values for the respective position, an inverted covariance matrix without performing a matrix inversion operation, either during run-time or prior to run-time.